Detecting infection using personalized criteria

ABSTRACT

A triage system that determines whether a user is likely to have contracted a disease based on sensor data received from a user device (e.g., a smartphone or activity tracker). Some symptoms are identified by comparing sensor data to a predetermined baseline and comparing the difference to a predetermined symptom threshold. Those comparisons are weighted to form a composite metric, which is compared to a composite threshold to determine if the user is likely to have contracted the disease. Because different individuals are at higher risk of contracting certain diseases, the determined based on specific characteristics of the user (e.g., potential comorbidities, age, gender, ethnic, racial, cultural, or economic status, geographic location, level of interaction with other individuals, etc.) that that have been identified in medical literature as increasing the individual&#39;s risk.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/878,433, filed May 19, 2020, which claims priority to U.S. Prov. Pat.Appl. No. 63/019,025, filed on May 1, 2020, which is hereby incorporatedby reference in its entirety.

FEDERAL FUNDING

None

BACKGROUND

Viral pandemics (like the COVID-19 pandemic) require identification ofinfected individuals (testing) and identification of those who have hadrecent contact with infected individuals (contact tracing). However, thenumber of definitive virus tests and conventional testing sites arelimited. Visiting medical caregivers or test center locations increasesexposure to both caregivers and individuals being tested. Meanwhile,conventional contact tracing is a laboriously time-consuming processthat requires a human to manually investigate the movements of eachinfected individual.

To address those concerns, definitive virus tests should be distributedto those at greatest risk, especially if the reservoir of infectedindividuals in the general population is unknown and determination ofdepth of reservoir is essential to safely manage population health.However, triaging potentially infected individuals is difficult for bothhealth care practitioners and patients. Patients are oftenunsophisticated, untrained, and stressed. Patients at home do not haveaccess to a medical practitioner and diagnostic equipment to make aninformed determination of the likelihood that she or he has contractedthe disease. The medical consensus regarding the symptoms that areassociated with a novel disease can change over time.

Modern smartphones often include sensors that can be used to monitor themovements and environment of an individual. Wearable fitness trackingdevices include even more sophisticated sensors that can be used tocontinually monitor the physiological parameters of users. Even whensymptoms cannot be directly determined through sensor data, surrogatesmay be used to infer those symptoms. Meanwhile, software-based systemsfor triaging potential patients can be updated as new evidence emergesregarding the symptoms that are associated with a novel disease.

Finally, smartphones and wearable fitness tracking devices can be usedto track the location of individuals and the other devices andindividuals in those locations.

Accordingly, there is a need for a distributed system that detects thesymptoms of a novel disease, using existing sensor technology andsurrogates, and evaluates the likelihood that the user has contractedthe novel disease. Ideally, the distributed system would makedeterminations based on the specific characteristics of the user (e.g.,a potential comorbidity, a demographic characteristic, the geographiclocation of the user, the user's level of interaction with otherindividuals, etc.) that have been identified in medical research asbeing correlated with instances of the novel disease. Furthermore, thedistributed system would ideally provide a flexible platform such thatsoftware-based determinations can be revised as new medical evidence isrevealed regarding the correlations between specific symptoms and thenovel disease. Finally, the distributed system would ideally be capableof identifying and contacting those who have recently been in contactwith individuals who are determined to have likely contracted the noveldisease.

SUMMARY

In view of the aforementioned needs, a triage system is provided thatdetermines whether a user is likely to have contracted a disease, basedon sensor data received from a user device (e.g., a smartphone). Thesensor data may be collected by an activity tracker in communicationwith the smartphone. The triage system identifies symptom(s) of thedisease comparing sensor data to a predetermined baseline and comparingthe difference between the sensor data and the predetermined baseline topredetermined symptom threshold(s).

The threshold for whether a specific user is likely to have contractedthe disease may be determined by the triage system based on specificcharacteristics of the user, such as age, gender, ethnic, racial,cultural, socio-economic status, personal comorbidities, etc., that havebeen identified in medical literature as increasing the individual'srisk. Similarly, geographically related indicators such as outbreakvolume in a given location, e.g., New York City, and the user's level ofinteraction with other people might likewise be incorporated.

As direct measurement of symptoms using the sensors available to theuser may not be feasible or sufficiently accurate, the triage systemalso uses surrogates to identify certain symptoms. For example, thetriage system may identify a fever based on heart rate or heart ratevariability data. The triage system may identify a cough by recordingsound and performing sound analysis of the recorded sound. The triagesystem may identify fatigue by analyzing the movement of the smartphone(or activity tracker). The triage system may identify shortness ofbreath (dyspnea) by analyzing respiratory rate data or pulse oxygenationdata or recording sound and performing sound analysis of the recordedsound. The triage system may identify loss of smell or taste byrecording sound and using speech detection algorithms to identifyphrases in the recorded sound indicative of loss of smell or taste.

The triage system may also assist in contact tracing by recording thelocations and networks joined by the user device. In response to adetermination that the user is likely to have contracted the disease,the triage system may identify additional devices that were in closeproximity with the user device (e.g., within a predetermined distance,within Bluetooth range, joined to the same local area network, etc.).The triage system may then be used to output information to thosedevices.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of exemplary embodiments may be better understood with referenceto the accompanying drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of exemplary embodiments.

FIG. 1 is a diagram of an architecture of a COVID-19 triage systemaccording to an exemplary embodiment of the present invention.

FIG. 2 is a block diagram of a user device according to an exemplaryembodiment of the present invention.

FIG. 3 is a block diagram of a wearable electronic device according toan exemplary embodiment of the present invention.

FIG. 4 is a flowchart of a disease detection and contact tracing processaccording to an exemplary embodiment of the present invention

DETAILED DESCRIPTION

Reference to the drawings illustrating various views of exemplaryembodiments of the present invention is now made. In the drawings andthe description of the drawings herein, certain terminology is used forconvenience only and is not to be taken as limiting the embodiments ofthe present invention. Furthermore, in the drawings and the descriptionbelow, like numerals indicate like elements throughout.

System Architecture

FIG. 1 is a diagram of an architecture 100 of a COVID-19 triage systemaccording to an exemplary embodiment of the present invention.

As shown in FIG. 1, the architecture 100 includes a server 180 thatcommunicates with a user device 200 (e.g., a smartphone) via one or morenetworks 150. Some users may also have access to a wearable electronicdevice 300 (for example, a fitness watch). The wearable electronicdevice 300 may communicate with the user device 200 via direct, shortrange, wireless communication signals 156 (e.g., Bluetooth).Additionally, some users may also have access to a body temperaturethermometer 110 with functionality to wirelessly communicate with theuser device 200 (e.g., via direct, short range, wireless communicationsignals 156 such as Bluetooth). Finally, some users may also have accessto a sensor-enabled mattress or bed 120 with functionality tocommunicate with the user device 200 (e.g., via the local area network152).

The server 180 may be any suitable computing device including, forexample, an application server or a web server. The server 180 includesnon-transitory computer-readable storage media, such as a hard disk orsolid-state memory, and at least one hardware computer processor. Inaddition to internal memory, the server 180 may store data on external(non-transitory) storage media, which may be accessed via a wiredconnection, via a local area network, etc.

The networks 150 may include any combination of the Internet 152,cellular networks, wide area networks (WAN), local area networks (LAN)154, etc. Communication via the networks 150 may be realized by wiredand/or wireless connections.

The body temperature thermometer 110 may be any suitable deviceconfigured to sense the body temperature of the user and outputinformation indicative of the user body temperature (as described, forexample, in U.S. Pat. Pub. No. 2017/0061074 to Singh, et al.). The bodytemperature thermometer 110 may output information indicative of theuser body temperature to the user device 200, for example, via direct,short range, wireless communication signals 156 (e.g., Bluetooth), viathe local area network 152, etc.

The mattress or bed 120 may be any suitable device configured todetermine the sleep state of a user lying on the mattress or bed 120 andoutput information indicative of the user sleep pattern (as described,for example, in U.S. Pat. Pub. No. 2014/0259434 to Nunn, et al.). Themattress or bed 120 may also detect the sleep posture of the user (e.g.,prone, reclining at angle, etc.). The sleep posture of the user may becorelated with respiratory function.

FIG. 2 is a block diagram of the user device 200 according to anexemplary embodiment of the present invention. The user device 200 maybe any suitable electronic device configured to send and/or receive datavia the networks 150. The user device 200 may be, for example, asmartphone, a personal computer, a notebook computer, a personal digitalassistant (PDA), a tablet, a network-connected vehicle, a wearabledevice, etc.

As shown in FIG. 2, the user device 200 includes at least one hardwareprocessor 210 and memory 220. The user device 200 may also include oneor more sensors 240 and one or more transceivers 260, which may bewireless transceivers. The sensor(s) 240 may include a microphone 242,one or more motion sensors 250 that detect motion by and/or orientationof the user device 200 (e.g., one or more multi-axes accelerometersand/or a gyroscope), a location sensor 256 (e.g., a global positionsystem (GPS) chip), etc. The transceiver(s) 260 may include a shortrange, direct transceiver 266 (e.g., to communicate via Bluetooth)and/or a longer-range wireless transceiver 264 (e.g., to communicate viathe local area network 154), etc.

FIG. 3 is a block diagram of the wearable electronic device 300according to an exemplary embodiment of the present invention.

As shown in FIG. 3, the wearable electronic device 300 includes at leastone hardware processor 310 and memory 320. The wearable electronicdevice 300 may also include one or more sensors 340. The sensor(s) 340may include a heart monitor 342 (e.g., a pulse sensor located proximatethe radial artery as described in U.S. Pat. Pub. No. 2015/0250396 toAhmed, et al.), a pulse oximetry sensor 344 (e.g., as described in U.S.Pat. Pub. No. 2015/0250396 to Ahmed, et al.), a skin temperaturethermometer 346 (e.g., as described in U.S. Pat. Pub. No. 2015/0250396to Ahmed, et al.), an electrodermal activity (EDA) sensor 347, sometimesreferred to as a Galvanic Skin Response (GSR) sensor (e.g., as describedin U.S. Pat. Pub. No. 2015/0250396 to Ahmed, et al.), a respiratorysensor 348 (e.g., as described in Hilett, O. S. et al.¹), one or moremotion sensors 350, such as one or more multi-axes accelerometers and/ora gyroscope (e.g., as described in U.S. Pat. Pub. No. 2015/0250396 toAhmed, et al.), etc. The wearable electronic device 300 may also includea wireless transmitter 360 that outputs information from the one or moresensors 340 to user device 200, for example, via direct, short range,wireless communication signals 156 (e.g., Bluetooth), via the local areanetwork 152, etc. ¹ Hoilett, O. S., et al. (2018, July). Kick LL: ASmartwatch for Monitoring Respiration and Heart Rate usingPhotoplethysmography. 2018 40th Annual International Conference of theIEEE Engineering in Medicine and Biology Society (EMBC).https://doi.org/10.1109/embc.2018.8513356

Briefly referring back to FIG. 1, the server 180 may provide a softwareprogram (e.g., a smartphone application) that is downloadable to theuser device 200. The user device 200 and the server 180 may thencooperatively perform the software processing steps described below.

Health Monitoring

According to a systematic review and meta-analysis published on Apr. 14,2020,² COVID-19 patients exhibit fever (86 percent of cases), cough (67percent of cases), fatigue (42 percent of cases), and difficult orlabored breathing, known as dyspnea (21 percent of cases). Additionally,it was found on Apr. 16, 2020,³ that 47 percent of COVID-19 patientsexhibit partial loss of sense of smell, known as hyposmia, or completeloss of smell, known as anosmia. ² Hu, Y., et al. (2020). Prevalence andseverity of corona virus disease 2019 (COVID-19): A systematic reviewand meta-analysis. Journal of Clinical Virology, 127, 104371.https://doi.org/10.1016/j.jcv.2020.104371³ Klopfenstein, T., et al.(2020). Features of anosmia in COVID-19. Médecine et MaladiesInfectieuses. https://doi.org/10.1016/j.medma1.2020.04.006

The COVID-19 triage system assesses the likelihood that the user hascontracted COVID-19—and, by extension, the urgency with which theparticular user should be tested directly—by detecting theaforementioned symptoms. In some instances, sensor data from the userdevice 200 (or wearable electronic device 300) may provide the COVID-19triage system with direct evidence that the user is experiencing one ofthe symptoms. However, sensor data that provides direct evidence may notbe available. Furthermore, even when direct sensor readings areavailable, that sensor data may not be sufficiently accurate fordiagnostic purposes. Accordingly, the COVID-19 triage system also usessurrogates (i.e., indirect evidence that the user is experiencing one ofthe symptoms) to detect symptoms.

By relying on well established, medically documented, dominant symptoms(and discretizing measures that calibrate them), a machine learnable,interpretable assessment score can be computed. Using ethnic, racial,cultural, socio-economic factors and personal health comorbidities,thresholds that indicate disease severity (e.g., uninfected, mild,moderate, and severe) can be established. As additional researchregarding COVID-19 is published, the COVID-19 triage system provides aplatform that can be updated so that the thresholds reflect the latestunderstanding of COVID-19 symptoms. In the event of a future epidemic orpandemic, the disclosed system provides a platform that can also be usedto recognize the symptoms of a future virus, bacteria, parasite,engineered bioweapon, etc.

Like any evaluation system, the COVID-19 triage system recognizesdeviations from established baselines. The COVID-19 triage system isinitially configured with baseline metrics derived from the generalpopulation. For example, after the age of 10, the resting heart rate ofan individual should range within 60 to 100 beats per minute with aregular rhythm; the normal body temperature should be approximately 37.0degrees Celsius (98.6 degrees Fahrenheit). However, generalized normsmay not be sufficient or fully accurate for diagnostic purposes. Thenormal body temperatures of individuals can range from 36.1 to 37.2degrees Celsius. Similarly, blood pressure can vary from 90/60 mm Hg to120/80 mm Hg. Therefore, when compared to generalized norms, an adultwith a heart rate of 90 beats per minute, a body temperature of 37.2degrees Celsius, or blood pressure of 119/79 mm Hg would be considerednormal. However, those same symptoms would be considered alarming if theindividual's personalized baseline were a heart rate of 60 beats perminute or a temperature of 36.1 degrees Celsius or a blood pressure of91/61 mm Hg. Therefore, the COVID-19 triage system is preferably used tofirst obtain an individualized baseline for each user.

The COVID-19 triage system preferably establishes personalized baselinemetrics for the user over a period of time while the user is known to behealthy. During this system training (onboarding) period, personalizedbaseline metrics for the user may be determined, including heart rate,blood pressure, pulse oxygen level, respiratory rhythm/rate, gaitanalysis (pace and frequency), sleep patterns, speech patterns, and/orother behavioral readings. The COVID-19 triage system may employ anactive learning approach to continuously monitor the user over a periodof time while the user is known to be healthy and make appropriateadjustments to the personalized baseline metrics for the user. If theCOVID-19 triage system is not given the opportunity to perform systemtraining while a specific user is known to be healthy (for example, ifthe specific user only starts using the COVID-19 triage system after thespecific user suspects that she or he may be infected), the COVID-19triage system may use default baseline metrics derived from the generalpopulation.

A method for detecting COVID-19 is described below using the fivedominant symptoms identified by the two established, medically vettedarticles described above; namely, fever, cough, fatigue, dyspnea, andanosmia. For each symptom, sensor-based measurements are taken (ifavailable) and surrogates are used. As additional research regardingCOVID-19 is published, the COVID-19 triage system provides a platformthat may be used to identify additional symptoms (either through directmeasurement or via surrogates). The COVID-19 triage system may also beupdated so that the thresholds reflect the latest understanding ofCOVID-19 symptoms. In the event of a future epidemic or pandemic, thedisclosed system can also be used to recognize the symptoms of a futurevirus or bacteria.

Fever detection. If available, a direct body temperature reading iscollected and compared to the previously established baseline for theuser. For example, body temperature readings from a body temperaturethermometer 110 with wireless communication functionality may betransmitted to the user device 200 (e.g., via Bluetooth). Since a bodytemperature thermometer 110 with wireless communication functionalitymay not be available, the COVID-19 triage system may periodically promptthe user via the user device 200 graphical user interface to take his orher body temperature and manually input the body temperature reading viathe user device 200 graphical user interface.

To continuously monitor the user without requiring the user to manuallyand periodically take her or his body temperature, a fever surrogate maybe used. An increased heart rate is an indication of fever.⁴ Therefore,the COVID-19 triage system may detect a fever by detecting the user'sheart rate and comparing the detected heart rate to the baseline heartrate for the user. For example, data indicative of the heart activity ofthe user may be received, for example, from the heart monitor 342. TheCOVID-19 triage system may then determine the R-R interval of the user'sheart activity, for example using a peak detection algorithm and/or afrequency analysis algorithm. ⁴ Karjalainen, J., et al. (1986). Feverand cardiac rhythm. JAMA Internal Medicine 1986 June; 146(6):1169-71.https://www.ncbinlm.nih.gov/pubmed/2424378

Another sample fever surrogate is heart rate variability. Therefore, theCOVID-19 triage system may detect a fever by performing a personalizedheart rate variability analysis across periods when the user is activeand periods when the user is inactive (at rest). Heart rate variabilitymay be determined, for example, based on data received from the heartmonitor 342. User activity may be determined, for example, based on datareceived from the motion sensors 250 of the user device 200 (or themotion sensors 350 of the wearable electronic device 300). For example,a spectral analysis via fast Fourier transforms may be used to determineif the heart rate of the user has increased.⁵ ⁵ Li, K. et al. (2019).Spectral Analysis of Heart Rate Variability: Time Window Matters.Frontiers in Neurology, 10. https://doi.org/10.3389/fneur.2019.00545

The difference between the user health metrics (body temperature, heartrate, or heart rate variability) and the (generalized or personalized)baseline is then converted into a single unitary metric indicative ofthe presence of (and, preferably, the probability or severity of) afever. In some embodiments, the difference between the user healthmetric and the baseline may be compared to a threshold determined by theCOVID-19 triage system to be indicative of the presence of a fever ormultiple thresholds determined by the COVID-19 triage system to beindicative of the probability or severity of a fever. (In otherembodiments, the differences between user health metrics and baselinesmay be clustered.) For example, the differences between the user healthmetric(s) and the baseline(s) may be compared to threshold(s) todetermine one of the following unitary fever metrics:

[ Table 1] Metric Description 1 Health metrics inconsistent with fever 2Heart rate variability impaired consistent with fever 3 Heart ratevariability impaired with reported fever

Cough detection. Coughs may be identifiable via acoustic engineeringcoupled with artificial intelligence. Using sound analysis, respiratoryconditions are identifiable. For example, the detection of periodicparoxysmal (frequent and violent) coughing and irregular respirations ispossible via the analysis of cough tonal quality.⁶ Therefore, theCOVID-19 triage system may record sound (via the microphone 242) and useartificial intelligence to detect coughing by the user. ⁶ Porter, P., etal. (2019). A prospective multicentre study testing the diagnosticaccuracy of an automated cough sound centred analytic system for theidentification of common respiratory disorders in children. RespiratoryResearch, 20(1). https://doi.org/10.1186/s12931-019-1046-6

Again, the COVID-19 triage system identifies a single unitary metricindicative of the presence of (and, preferably, the probability orseverity of) a cough. In this instance, determination is made byperforming a sound analysis (e.g., using artificial intelligence). Forexample, the COVID-19 triage system may identify one of the followingunitary cough metrics:

[Table 2] Metric Description 1 Deviation from baseline not detected 2Deviation from baseline indicates possible cough

Fatigue detection. The COVID-19 triage system may detect fatigue byidentifying deviations from the normal activity of the user.Additionally, the COVID-19 triage system may detect slowing or stoppingalong any daily movement, increasing the likelihood that the user isexperiencing fatigue. Finally, the COVID-19 triage system may detectfatigue by comparing the amount of sleep the user is experiencingrelative to the personalized baseline of the user (or determining if theuser is sleeping for more than nine hours). User movement may bedetermined, for example, based on data received from the motion sensors250 of the user device 200 (or the motion sensors 350 of the wearableelectronic device 300). Whether the user is asleep may be determinedbased in drop skin temperature (for example, as measured by the skintemperature thermometer 346), galvanic skin response data (for example,as measured by the electrodermal activity (EDA) sensor 347), lack ofactivity (for example, according to data collected by the motion sensors250) and/or reduced heart rate (for example, as measured by the heartmonitor 342). Where available, whether the user is asleep may bedetermined based on data received from the sensor-enabled mattress orbed 120.

Again, the difference between the user health metrics and the(generalized or personalized) baselines are converted into a singleunitary metric indicative of the presence of (and, preferably, theprobability or severity of) fatigue. For example, the COVID-19 triagesystem may identify one of the following unitary fatigue metrics:

[Table 3] Metric Description 1 Movement inconsistent with fatigue 2Movement consistent with mild fatigue 3 Movement consistent with fatigueand excessive sleep

Dyspnea detection. Difficulty or labored breathing (dyspnea) may bedetected in multiple ways. If the relevant sensor data are available,the COVID-19 triage system may detect dyspnea by identifying anincreased respiratory rate (for example, as measured by the respiratorysensor 348) and/or a change in blood oxygenation (for example, asmeasured by the pulse oximetry sensor 344). Additionally, as describedabove for detecting cough, acoustic engineering coupled with artificialintelligence may process breathing sounds to detect difficultybreathing. Also, the COVID-19 triage system may prompt the user (eitheraudibly via the user device 200 speaker or visually via the user device200 graphical user interface) to answer shortness of breath assessmentquestions (e.g., “How do you feel when walking to the bathroom?”) andreceive responses (either audibly via the user device 200 microphone orvisually via the user device 200 graphical user interface). Again, theCOVID-19 triage system may use the user health metrics or user responsesto identify a single unitary metric indicative of the presence of (and,preferably, the probability or severity of) a dyspnea. For example, theCOVID-19 triage system may identify one of the following unitary dyspneametrics:

[Table 4] Metric Description 1 Shortness of breath not detected 2Breathing impaired, more with activity

Anosmia, hyposmia, ageusia, and hypogeusia detection. The loss of thesense of smell (anosmia) and reduced ability to smell (hyposmia) havewell established diagnostic tests, such as the University ofPennsylvania Smell Identification Test (UPSIT) and “Sniffin sticks”, atest of nasal chemosensory performance based on pen-like odor-dispensingdevices.⁷ However, these tests are not likely to be readily available tothe typical user. Therefore, the COVID-19 triage system employs asurrogate detection approach. ⁷ Kobal, G., et al. (1996). “Sniffin′sticks”: screening of olfactory performance. Rhinology. 1996 December;34(4):222-6. https://www.ncbinlm.nih.gov/pubmed/9050101

The COVID-19 triage system may use passive vocal monitoring to determineif the user is making subjective complaints that are indicative ofanosmia or hyposmia. For example, the COVID-19 triage system may recordsound (captured by the microphone 242) and use speech detectionalgorithms to identify phrases like “I can't smell my coffee” or “Ican't smell my breakfast”, which are indicative of a loss of the senseof smell. Additionally or alternatively, the COVID-19 triage system mayprompt the user (either audibly via the user device 200 speaker orvisually the via the user device 200 graphical user interface) to answerquestions about the user's capacity to smell. For example, the COVID-19triage system may ask “Can you smell your toothpaste?” or “Can you smellyour spice bottle of cinnamon?” or “Can you smell the cut onion?” andprovide functionality for the user to subjectively answer questions thatmay be used to detect a loss of smell.

Similar to anosmia and hyposmia, the loss of sense of taste (ageusia)and reduced ability to taste sweet, sour, bitter, or salty substances(hypogeusia) can likewise be detected via surrogates. The COVID-19triage system may use passive vocal monitoring to determine if the useris making subjective complaints that are indicative of ageusia orhypogeusia, such as “I can't taste my coffee” or “I can't taste mybreakfast”. Additionally or alternatively, the COVID-19 triage systemmay prompt the user (either audibly using the user device 200 speaker orvisually the via the user device 200 graphical user interface) to answerquestions about the user's capacity to smell, such as “Can you tasteyour toothpaste?” or “Can you taste salt or sugar?” or “Can you taste acut onion?”

Again, the COVID-19 triage system may use the user phrases or userresponses to identify a single composite metric indicative of thepresence of the loss of taste or smell. For example, the COVID-19 triagesystem may identify one of the following unitary metrics:

[Table 5] Metric Description 1 Smell (and/or taste) normal 2 Smell(and/or taste) impaired

For each unitary symptom metric described above, a higher unitarysymptom metric is indicative of a more severe (and/or higher likelihoodthat the user is experiencing) the respective individual COVID-19symptom.

Detecting COVID-19

The COVID-19 triage system determines the likelihood that the user hascontracted COVID-19 by weighing each of the unitary symptom metricsdescribed above. The unitary metrics for each of the COVID-19 symptomsmay be combined to form a composite metric indicating the likelihoodthat the user has contracted COVID-19. The unitary metric for eachsymptom may be weighted by the COVID-19 triage system based on theprevalence and correlation between each symptom and COVID-19, asidentified in the latest medical documentation. For example, based onthe two established, medically vetted articles described above, theweighting function employed by the COVID-19 triage system may be:

Fever*0.86+Cough*0.67+Fatigue*0.42+Dyspnea*0.21+Anosmia*0.47=

The composite metric for each user may be compared to one or morecomposite thresholds. The composite threshold(s) may be determined,based on the latest medical documentation, such that a composite metricbelow the lowest composite threshold is indicative of a low likelihoodthat the user has contracted COVID-19. Additional composite threshold(s)may be determined, again based on the latest medical documentation,indicative of higher likelihoods that the user has contracted COVID-19(or indicative that the user is experiencing more severe symptoms ofCOVID-19).

The composite threshold(s) may be determined based on an analysis of thegeneralized population and/or, more preferably, individualcharacteristics of the user. The individual characteristics of the usermay include age, gender, ethnic, racial, cultural, socio-economicstatus, personal comorbidities, etc. Geographically related indicatorssuch as outbreak volume in a given location, e.g., New York City, mightlikewise be incorporated. Additionally, users may be asked tosubjectively assess their level of interaction with other people, as themedical literature may indicate that more human interaction (forexample, by users that work in a grocery store or nursing some) may putthose users at higher risk. Given such considerations, the compositethreshold(s) for each individual may altered. For example, given apatient with hypertension and diabetes, a “high severity” conditioncomposite threshold might be set at a lower threshold value to accountfor the likely expected severity introduced by the comorbidities.Similarly, and possibly related, COVID-19 disproportionately affects theAfrican-American community; thus, again, lower thresholds might be setto account for disease predominance should the user be African-American.

Because COVID-19 is novel, new studies are being published indicatingnew symptoms of the disease. As shown in a comprehensive review ofCOVID-19 diagnosis and management published on Apr. 29, 2020,⁸assessment for diagnostic care changes daily and the preponderance ofsymptoms varies across studies: ⁸ Pascarella, G., et al. (2020).COVID-19 diagnosis and management: a comprehensive review. Journal ofInternal Medicine. https://doi.org/10.1111/joim.13091

[Table 1 of Pascarella, G., et al. (2020)] Guan et al. Chen et al. Shiet al. Huang et al. Yang et al. Patients (n) 1081 99 21 41 52 Fever 473(44%) 82 (83%) 40 (98%) 473 (44%) 46 (89%) Dyspnea 205 (19%) 31 (31%) 9(43%) 22 (54%) 33 (64%) Cough 745 (69%) 81 (82%) 15 (71%) 31 (76%) 40(77%) Sputum 370 (34%) — 3 (14%) 11 (27%) — Rhinorrhea 53 (5%) 4 (4%) 5(24%) — 3 (6%) Sore Throat 153 (14%) 5 (5%) — — — Headache 150 (14%) 8(8%) 2 (10%) 2 (5%) 3 (6%) Diarrhea 42 (4%) 2 (2%) 1 (5%) 1 (2%)Nausea/vomiting 55 (5%) 1 (1%) 2 (10%) — 2 (4%) Myalgia 164 (15%) 11(11%) — — 6 (12%)

To more accurately determine the likelihood that the user has contractedCOVID-19, the process performed by the COVID-19 triage system may beupdated to reflect the latest medical research. Therefore, the COVID-19triage system provides a flexible platform that may be updated toidentify additional symptoms that are found to be indicative ofCOVID-19. Those additional symptoms may be diagnosed by the COVID-19triage system via direct sensor measurement, via surrogates, and/orthrough user responses to directed assessment questions (e.g., via theuser device 200 graphical user interface). As additional studies arereleased, the COVID-19 triage system may also update the thresholds forthe unitary metrics describing each of the symptoms above, the weightsof each unitary metric, and/or the composite threshold(s).

The COVID-19 triage system may determine the likelihood that the userhas contracted COVID-19 in response to a request by the user (e.g., viathe user device 200 graphical user interface). The COVID-19 triagesystem may also be configured (with permission from the user) toperiodically or continuously monitor the health metrics of the user andperiodically determine (e.g., at least once per day) the likelihood thatthe user has contracted COVID-19.

The COVID-19 triage system may recommend that a user take certainactions based on the determined likelihood that the user has contractedCOVID-19. For example, in response to a composite metric below apredetermined threshold, the COVID-19 triage system may output anindication that the user is unlikely to have contracted COVID-19 and mayrecommend that the user forgo further testing. However, in response to acomposite metric greater than a predetermined threshold, the COVID-19triage system may output an indication that the user is presumed to havecontracted COVID-19 and recommend that the user obtain definitive virustesting and, later, serology testing for anti-Spike IgG.

To improve the COVID-19 triage system, users that are tested forCOVID-19 may be encouraged to indicate the results of their test (e.g.,via the user device 200 graphical user interface). Once a statisticallysignificant sample of user health metrics and user test results havebeen received, the COVID-19 triage system may refine the unitary metricthresholds, the weights of each unitary metric, and/or the compositethreshold(s) to better predict the likelihood that other users havecontracted COVID-19. The COVID-19 triage system may use a (deep orshallow) machine learning process to adjust the unitary metricthresholds, the unitary metric weights, and/or the compositethreshold(s).

Contact Tracing

The COVID-19 triage system may also be used to assist in a contacttracing process to identify persons who may have come into contact withusers that have been infected with COVID-19. Accordingly, withpermission of the user, the COVID-19 triage system may store recordsindicating the locations of the wearable electronic device 300 (ifavailable) and/or the user device 200 (e.g., as determined by thelocation sensor 256). The COVID-19 triage system may also store recordsindicating the devices within range of short-range transceiver 266 ofthe user device 200 or the wireless transmitter(s) 360 of the wearableelectronic device 300. The COVID-19 triage system may also store recordsindicating the local area networks 154 that the user device 200 (or thewearable electronic device 300) has joined.

In response to a determination by the COVID-19 triage system that theuser is likely to have contracted COVID-19 (e.g., based on the symptomsdescribed above), the COVID-19 triage system may determine the locationsof the user during a time period when the user was likely to have beeninfectious (e.g., seven days prior to the determination that the user islikely to have contracted COVID-19). The COVID-19 triage system may alsoidentify other devices in those locations during the time period whenthe user was likely to have been infectious. For example, the COVID-19triage system may identify devices within a predetermined distance ofthe locations of the wearable electronic device 300 and/or the userdevice 200, devices within range of short-range transceiver 266 of theuser device 200 or the wireless transmitter(s) 360 of the wearableelectronic device 300, devices joined to the same local area networks154 as the user device 200.

If the other devices in those locations are also employing the COVID-19triage system, the COVID-19 triage system can contact those users (e.g.,via the graphical user interface of their device, via email, etc.) toindicate that they may have been exposed to COVID-19 and make arecommendation to those users (e.g., that they should obtain definitivevirus testing). Additionally, with permission from the user, theCOVID-19 triage system may share device location information withorganizations performing contact tracing. Therefore, the data collectedby the COVID-19 triage system can contribute to a larger data set beingused to trace contacts between infected individuals and others. Forexample, the COVID-19 triage system may indicate that a user (who isdetermined to have likely contracted COVID-19) was within range ofanother device. However, that device may not have the COVID-19 triagesystem installed and so the COVID-19 triage system may not be able toidentify and contact the user of that device. However, the organizationsperforming contact tracing may be able to identify and contact the userof that device.

Users may also grant the COVID-19 triage system access to their calendardata and contact data (stored online or on the user device 200). TheCOVID-19 triage system may then be configured to contact individuals(e.g., using contact information stored on the user device 200 oraccessible via the Internet) if calendar data indicates that he or shehad an in-person meeting with a user that is presumed to have contractedCOVID-19 during the time period when the user was likely to have beeninfectious. The COVID-19 triage system may also be configured to searchthe Internet or other data sources (e.g., employing a mediator-orientedsearch as described in U.S. Pat. No. 6,904,428 to Frieder, et al.) forindividuals who may have had contact with the user during the timeperiod when the user was likely to have been infectious.

The COVID-19 triage system may request permission from a user beforecontacting individuals who have been in contact with the user. Differentmessage types may be sent to different individuals depending on thelevel of interaction, recency, length of contact, personal familiarity,etc.

Authority Data Transmission

Because global spread information is best derived from large scale datacollection, the COVID-19 triage system may be configured to outputinformation regarding user symptoms and/or behavioral patterns to agovernment or private health monitoring organization. Multipleelectronic health record formats may be supported to provide flexibilityof data transfer and subsequent analysis by the remote collecting party.The information transmitted (and the frequency of those transmissions)may be controlled by the user. Functionality may be provided for theuser to terminate or change the information transmitted and/or thefrequency of those transmissions at any time. Transmitted data may beanonymized. Data transmissions may be encrypted. The recipient of thosedata transmissions may be authenticated before the data are transmitted.

Privacy Protections

To preserve the user privacy, the COVID-19 triage system may requireuser registration and user permission before collecting sensor data anddevice location data. The COVID-19 triage system may also require userpermission to access to user contacts and calendar data. The COVID-19triage system may provide functionality for users to customize accessrights (i.e., to individually grant or deny access to specific datatypes). User privacy may be further protected by encrypting all datacollected, stored, and/or transmitted by the COVID-19 triage system. Alldata collected may be purged after a time period when that data nolonger have diagnostic value. For example, if the COVID-19 triage systemdetermines that the user is unlikely to have contracted COVID-19 asdescribed above, the COVID-19 triage system may purge allpreviously-collected sensor data, determinations regarding the user'ssymptoms, and user location data.

Whenever possible, the sensor data described above may be collected andprocessed by the user device 200. However, certain processes describedabove (such as artificial intelligence or machine learning processes)may require the server 180 to receive sensor data indicative of usersymptoms. In those instances, the sensor data transmitted by the userdevice 200 may be anonymized and/or encrypted.

Information and Incentives

The COVID-19 triage system is uniquely capable of providing valuablehealth-related information, which can be highly targeted based on thehealth status and location of the user. For example, the COVID-19 triagesystem may provide links to current literature and current status (e.g.,locations and waiting times) of treatment centers, etc.

Potential users may also be incentivized to use the COVID-19 triagesystem. A heath care organization may offer free or reduced cost testingor medical treatment if an individual is diagnosed using the COVID-19triage system. Monetary rewards may be provided to “friends and family.”Commercial vendors (e.g., pharmaceutical companies) may be motivated toprovide resources to users of the COVID-19 triage system to increaseawareness of their products.

One barrier to widespread adoption of the COVID-19 triage system may bethe availability of smart devices, such as smartphones and activitytrackers. As these devices are now inexpensive, they may be provided(either free or subsidized as economically necessary) to incentive useof the COVID-19 triage system. Additionally or alternatively, theCOVID-19 triage system may include web-based implementations that can berun on less expensive, Internet-connected devices (e.g., Chromebooks).

Language Support

English usage is common but not universal. Therefore, the COVID-19triage system may support multiple language interactions, potentiallyvia automated real-time translation based on deep learners as is knownin the art. Support may also be provided for “minimal use” languages tosupport cultural differences.

Process

FIG. 4 is a flowchart of a disease detection and contact tracing process400 according to an exemplary embodiment of the present invention. Asbriefly mentioned above, the process 400 may be performed by the server180 in conjunction with the user device 200 (e.g., running a softwareprogram provided by the server 180). As mentioned above, many of thesteps of the process 400 require user authorization.

Locations of the user device 200 (and, if available, the wearableelectronic device 300) are stored in step 410. The locations of the userdevice 200 may be determined by the location sensor 256. The locationsof the user device 200 (or the wearable electronic device 300) may alsoinclude the local area networks 154 that the user device 200 (or thewearable electronic device 300) has joined and the identify of otherdevices within range of short-range transceiver 266 of the user device200 or the wireless transmitter(s) 360 of the wearable electronic device300.

Sensor data are identified in step 420. The sensor data may be collectedby the sensors 240 of the user device, the sensors 340 of the wearableelectronic device 300, the body temperature thermometer 110, thesensor-enabled mattress or bed 120, etc.

Baselines are established in step 422. By default, baselines derivedfrom the general population may be used. More preferably, however,personalized baselines are established using sensor data collected fromthe user over a period of time while the user is known to be healthy.The process 400 then returns to step 420 and collects more sensor data.

The updated sensor data are compared to the baselines in step 424. Thedifferences between the sensor data and the baselines are compared tosymptom thresholds in step 426 to form symptom metrics 428. As describedabove, in some instances the sensor data may provide direct evidencethat the user is experiencing one of the symptoms. However, other sensordata also provides surrogates (indirect evidence that the user isexperiencing one of the symptoms) that can be used to form a symptommetric 428.

The symptom metrics 428 are weighted in step 430 to form a compositemetric 432. The composite metric 432 is compared to compositethreshold(s) in step 440 to determine the likelihood that the user hascontracted the disease. If, based on the composite metric 432, the useris unlikely to have been infected (step 450: No), the process 400returns to step 420 and updated sensor data are collected.

If, based on the composite metric 432, the user is likely to have beeninfected (step 450: Yes), the locations of the user device 200 (andwearable electronic device 300) during a time period when the user waslikely to have been infectious are identified in step 460. As mentionedabove, those locations may be geographic coordinates, local areanetworks 154 that the user device 200 (or the wearable electronic device300) joined, and the identity of other devices within range ofshort-range transceiver 266. Those device locations may be output to anorganization performing contact tracing in step 462.

Other devices in those locations during the time period when the userwas likely to have been infectious are identified in step 464. Thoseother devices may have been devices within a predetermined distance ofthe locations of the wearable electronic device 300 and/or the userdevice 200, within range of short-range transceiver 266 of the userdevice 200 or the wireless transmitter(s) 360 of the wearable electronicdevice 300, or devices joined to the same local area networks 154 as theuser device 200. Users of the other devices are contacted in step 470.

While a preferred embodiment has been set forth above, those skilled inthe art who have reviewed the present disclosure will readily appreciatethat other embodiments can be realized within the scope of the presentinvention. Disclosures of specific technologies are also illustrativerather than limiting. Therefore, the present invention should beconstrued as limited only by the claims.

1. A computer implemented method for determining whether a user islikely to have contracted a disease, the method comprising: identifyingone or more personalized characteristics of a user; receiving sensordata from a user device; for each of a plurality of symptoms of thedisease, calculating a symptom metric indicative of the likelihood thatthe user is experiencing the symptom, wherein at least one of theplurality of symptom metrics is calculated by comparing the sensor datato a predetermined baseline and comparing a difference between thesensor data and the predetermined baseline to at least one symptomthreshold; weighing each of the symptom metrics to form a compositemetric indicative of the likelihood that the user is likely to havecontracted the disease; identifying a composite threshold fordetermining if the user is likely to have contracted the disease basedon the one or more personalized characteristics of the user; anddetermining whether the user is likely to have contracted the disease bycomparing the composite metrics to the composite threshold.
 2. Themethod of claim 1, wherein the one or more personalized characteristicsused to determine the composite threshold for the user include apotential comorbidity of the user.
 3. The method of claim 1, wherein theone or more personalized characteristics used to determine the compositethreshold for the user include at least one of an age, gender, ethnic,racial, cultural, or economic status of the user.
 4. The method of claim1, wherein the one or more personalized characteristics used todetermine the composite threshold for the user include a geographiclocation of the user.
 5. The method of claim 1, wherein the one or morepersonalized characteristics used to determine the composite thresholdfor the user include the user's level of interaction with otherindividuals.
 6. The method of claim 1, further comprising: providingfunctionality to adjust one or more of the symptoms of the disease, oneor more of the predetermined symptom thresholds, the weights of one ormore of the symptom metrics, and/or one or more composite thresholds. 7.The method of claim 1, wherein at least one of the symptom metrics isidentified based on indirect evidence, received from the user device,that the user is experiencing the at least one symptom.
 8. The method ofclaim 7, wherein: one of the plurality of symptoms is a fever and theindirect evidence that the user is experiencing a fever comprisesinformation indicative of the heart rate or heart rate variability ofthe user; or one of the plurality of symptoms is fatigue and theindirect evidence that the user is experiencing fatigue comprisesmovement of the user device or a wearable device in communication withthe user device; or one of the plurality of symptoms is dyspnea and theindirect evidence that the user is experiencing dyspnea comprisesrespiratory rate or pulse oxygenation data; or one of the plurality ofsymptoms is anosmia or ageusia and the indirect evidence that the useris experiencing anosmia or ageusia comprises sound, recorded by amicrophone, that is analyzed using speech detection algorithms toidentify phrases in the recorded sound indicative of anosmia or ageusia.9. The method of claim 22, wherein receiving the locations of the userdevice and identifying additional devices in the location of the userdevice comprises: identifying the additional devices within range of theuser device via direct, short range, wireless communication signals; oridentifying local area networks joined by the user device and theadditional devices joined to the local area network.
 10. A system fordetermining whether a user is likely to have contracted a disease, themethod comprising: a server in network communication with a user device;a software application that is downloadable to and executable by theuser device; non-transitory computer readable storage media storinginstructions that, when executed by the server and/or the user device,cause the system to: identify one or more personalized characteristicsof a user; receive sensor data from the user device; for each of aplurality of symptoms of the disease, calculate a symptom metricindicative of the likelihood that the user is experiencing the symptom,wherein at least one of the plurality of symptom metrics is calculatedby comparing the sensor data to a predetermined baseline and comparing adifference between the sensor data and the predetermined baseline to atleast one symptom threshold; weigh each of the symptom metrics to form acomposite metric indicative of the likelihood that the user is likely tohave contracted the disease; identify a composite threshold fordetermining if the user is likely to have contracted the disease basedon the one or more personalized characteristics of the user; anddetermine whether the user is likely to have contracted the disease bycomparing the composite metrics to the composite threshold.
 11. Thesystem of claim 10, wherein the one or more personalized characteristicsused to determine the composite threshold for the user include apotential comorbidity of the user.
 12. The system of claim 10, whereinthe one or more personalized characteristics used to determine thecomposite threshold for the user include at least one of an age, gender,ethnic, racial, cultural, or economic status of the user.
 13. The systemof claim 10, wherein the one or more personalized characteristics usedto determine the composite threshold for the user include a geographiclocation of the user.
 14. The system of claim 10, wherein the one ormore personalized characteristics used to determine the compositethreshold for the user include the user's level of interaction withother individuals.
 15. The system of claim 10, wherein the serverprovides functionality to adjust one or more of the symptoms of thedisease, one or more of the predetermined symptom thresholds, theweights of one or more of the symptom metrics, and/or one or morecomposite thresholds.
 16. The system of claim 10, wherein at least oneof the symptom metrics is identified based on indirect evidence,received from the user device, that the user is experiencing the atleast one symptom.
 17. The system of claim 16, wherein: one of theplurality of symptoms is a fever and the indirect evidence that the useris experiencing a fever comprises information indicative of the heartrate or heart rate variability of the user; or one of the plurality ofsymptoms is fatigue and the indirect evidence that the user isexperiencing fatigue comprises movement of the user device or a wearabledevice in communication with the user device; or one of the plurality ofsymptoms is dyspnea and the indirect evidence that the user isexperiencing dyspnea comprises respiratory rate or pulse oxygenationdata; or one of the plurality of symptoms is anosmia or ageusia and theindirect evidence that the user is experiencing anosmia or ageusiacomprises sound, recorded by a microphone, that is analyzed using speechdetection algorithms to identify phrases in the recorded soundindicative of anosmia or ageusia.
 18. The system of claim 24, whereinreceiving the locations of the user device and identifying additionaldevices in the location of the user device comprises: identifying theadditional devices within range of the user device via direct, shortrange, wireless communication signals; or identifying local areanetworks joined by the user device and the additional devices joined tothe local area network.
 19. (canceled)
 20. (canceled)
 21. The method ofclaim 7, wherein one of the plurality of symptoms is a cough and theindirect evidence that the user is experiencing a cough comprises sound,recorded by a microphone of the user device, that is analyzed toidentify sounds indicative of a cough.
 22. The method of claim 1,further comprising: receiving and storing locations of the user device;and in response to a determination that the user is likely to havecontracted the disease, identifying additional devices in the locationof the user device and outputting information to the users of theadditional devices.
 23. The system of claim 16, wherein one of theplurality of symptoms is a cough and the indirect evidence that the useris experiencing a cough comprises sound, recorded by a microphone of theuser device, that is analyzed to identify sounds indicative of a cough.24. The system of claim 10, wherein the instructions further cause thesystem to: store locations of the user device; and in response to adetermination that the user is likely to have contracted the disease,identifying additional devices in the location of the user device andoutputting information to the users of the additional devices.